Abstract:
The study is conducted to optimize technologies for automated control of heat supply systems based on renewable energy sources that can increase energy efficiency and reduce environmental impact. The study uses machine learning methods for predicting heat energy consumption, intelligent monitoring and diagnostics systems, and control automation algorithms to optimize the operation of heat supply systems based on renewable energy sources. As a result of the study, an automated heat supply management system based on renewable energy sources is analyzed, which demonstrated high energy efficiency and flexibility in operation. The use of intelligent algorithms allows optimising the distribution of heat energy, considering fluctuations in weather conditions and loads. Automation of control processes reduces operating costs and minimizes human intervention. It is also established that the integration of solar collectors and geothermal sources into a single system reduces dependence on traditional energy sources and carbon dioxide emissions. The study shows that optimizing the use of renewable sources with automated control not only increases the reliability of heat supply but also contributes to reducing operating costs in comparison with traditional systems. This confirms the prospects of such technologies for broad application in municipal and industrial heat supply systems. In addition, it is determined that automated control systems contribute to more accurate forecasting of thermal energy needs, which reduces the risk of overloads and interruptions in heat supply. The study also shows that the use of combined sources of renewable energy, such as solar and geothermal installations, increases the overall efficiency of the system.
